In this paper, we directly compare the federated learning performance of two widely used ASR model types, i.e. hybrid ASR based on the Pytorch-Kaldi system [1]} and the E2E transformer/CTC model, trained via ESPnet [2]}. We use the Librispeech corpus [3]} for all experiments, which contains \([960]{h}\)  of speech data from 2484 speakers. Specifically, we consider a use case, where every client node only possesses data of one speaker, although scenarios with multiple speakers per client are conceivable (e.g. cross-silo federated learning in [4]}).
